System for calibrating a PTT-based blood pressure measurement using arm height

ABSTRACT

The invention provides a system and method for measuring vital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) and motion (e.g. activity level, posture, degree of motion, and arm height) from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient&#39;s heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient&#39;s body to which it is affixed. A processing component, typically worn on the patient&#39;s body and featuring a microprocessor, receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.

CROSS REFERENCES TO RELATED APPLICATION

Not Applicable

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to medical devices for monitoring vitalsigns, e.g., arterial blood pressure.

2. Description of the Related Art

Pulse transit time (PTT), defined as the transit time for a pressurepulse launched by a heartbeat in a patient's arterial system, has beenshown in a number of studies to correlate to systolic (SYS), diastolic(DIA), and mean (MAP) blood pressure. In these studies, PTT is typicallymeasured with a conventional vital signs monitor that includes separatemodules to determine both an electrocardiogram (ECG) and pulse oximetry(SpO2). During a PTT measurement, multiple electrodes typically attachto a patient's chest to determine a time-dependent ECG componentcharacterized by a sharp spike called the ‘QRS complex’. The QRS complexindicates an initial depolarization of ventricles within the heart and,informally, marks the beginning of the heartbeat and a pressure pulsethat follows. SpO2 is typically measured with a bandage orclothespin-shaped sensor that attaches to a patient's finger, andincludes optical systems operating in both the red and infrared spectralregions. A photodetector measures radiation emitted from the opticalsystems that transmits through the patient's finger. Other body sites,e.g., the ear, forehead, and nose, can also be used in place of thefinger. During a measurement, a microprocessor analyses both red andinfrared radiation detected by the photodetector to determine thepatient's blood oxygen saturation level and a time-dependent waveformcalled a photoplethysmograph (‘PPG’). Time-dependent features of the PPGindicate both pulse rate and a volumetric absorbance change in anunderlying artery caused by the propagating pressure pulse.

Typical PTT measurements determine the time separating a maximum pointon the QRS complex (indicating the peak of ventricular depolarization)and a foot of the optical waveform (indicating the beginning thepressure pulse). PTT depends primarily on arterial compliance, thepropagation distance of the pressure pulse (which is closelyapproximated by the patient's arm length), and blood pressure. Toaccount for patient-dependent properties, such as arterial compliance,PTT-based measurements of blood pressure are typically ‘calibrated’using a conventional blood pressure cuff. Typically during thecalibration process the blood pressure cuff is applied to the patient,used to make one or more blood pressure measurements, and then left onthe patient. Going forward, the calibration blood pressure measurementsare used, along with a change in PTT, to determine the patient's bloodpressure and blood pressure variability. PTT typically relates inverselyto blood pressure, i.e., a decrease in PTT indicates an increase inblood pressure.

The PPG, like most signals detected with optical means, is stronglyaffected by motion of the patient. For example, if the pulse oximeter isplaced on the patient's finger, then motion of the finger can influenceboth the blood flow and degree of ambient light detected by theoximeter's optical system. This, in turn, can add unwanted noise to thePPG.

A number of issued U.S. patents describe the relationship between PTTand blood pressure. For example, U.S. Pat. Nos. 5,316,008; 5,857,975;5,865,755; and 5,649,543 each describe an apparatus that includesconventional sensors that measure an ECG and PPG, which are thenprocessed to determine PTT. U.S. Pat. No. 5,964,701 describes afinger-ring sensor that includes an optical system for detecting a PPG,and an accelerometer for detecting motion.

SUMMARY OF THE INVENTION

This invention provides a body-worn vital sign monitor featuring aseries of sensors that measure time-dependent PPG, ECG, motion (ACC),and pressure waveforms to continuously monitor a patient's vital signs,degree of motion, posture and activity level. Blood pressure, a vitalsign that is particularly useful for characterizing a patient'scondition, is typically calculated from a PTT value determined from thePPG and ECG waveforms. Once determined, blood pressure and other vitalsigns can be further processed, typically with a server within ahospital, to alert a medical professional if the patient begins todecompensate. Processing the combination of the patient's motion andvital sign information is particularly useful, as these components areintegrally related: a patient that is walking, for example, may have anaccelerated heart rate and is likely breathing; the system can thus bedesigned to not alarm on these parameters, even if they exceedpredetermined, preprogrammed levels.

In one aspect, the above-described systems provide a body-worn vitalsign monitor that features an optical sensor, typically worn on thepatient's finger, which includes a light source that emits radiation anda photodetector that detects radiation after it irradiates a portion ofthe patient's body to generate a time-dependent PPG. The monitor alsoincludes an electrical sensor featuring at least two electrodes thatmeasure electrical signals from the patient's body, and an electricalcircuit that receives the electrical signals and processes them todetermine a time-dependent ECG. To determine the patient's motion,posture, and activity level, the monitor features at least threemotion-detecting sensors, each configured to be worn on a differentlocation on the patient's body and to generate at least onetime-dependent ACC waveform. The electrical sensor and the threemotion-detecting sensors are typically included in a cable system wornon the patient's arm. A processing component within the monitor connectsto cable system to receive the ECG, PPG, and the at least one ACCwaveform generated by each motion-detecting sensor. The processingcomponent operates a first algorithm that processes the PPG and ECG todetermine a time difference between a time-dependent feature in eachwaveform; a second algorithm that processes the time difference todetermine a blood pressure value; and a third algorithm thatcollectively processes the ACC waveforms generated by eachmotion-detecting sensor to determine at least one ‘motion parameter’(e.g. the patient's posture, activity level, arm height, and degree ofmotion). A wireless system within the monitor transmits the patient'sblood pressure value and the motion parameter to a remote receiver.

In other embodiments, PTT can be calculated from time-dependentwaveforms other than the ECG and PPG, and then processed to determineblood pressure. In general, PTT can be calculated by measuring atemporal separation between features in two or more time-dependentwaveforms measured from the human body. For example, PTT can becalculated from two separate PPGs measured by different optical sensorsdisposed on the patient's fingers, wrist, arm, chest, or virtually anyother location where an optical signal can be measured using atransmission or reflection-mode optical configuration. In otherembodiments, PTT can be calculated using at least one time-dependentwaveform measured with an acoustic sensor, typically disposed on thepatient's chest. Or it can be calculated using at least onetime-dependent waveform measured using a pressure sensor, typicallydisposed on the patient's bicep, wrist, or finger. The pressure sensorcan include, for example, a pressure transducer, piezoelectric sensor,actuator, polymer material, or inflatable cuff.

In one aspect, the invention provides a system and method for measuringvital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) andmotion (e.g. activity level, posture, degree of motion, and arm height)from a patient. The system features: (i) first and second sensorsconfigured to independently generate time-dependent waveforms indicativeof one or more contractile properties of the patient's heart; and (ii)at least three motion-detecting sensors positioned on the forearm, upperarm, and a body location other than the forearm or upper arm of thepatient. Each motion-detecting sensor generates at least onetime-dependent motion waveform indicative of motion of the location onthe patient's body to which it is affixed. A processing component,typically worn on the patient's body and featuring a microprocessor,receives the time-dependent waveforms generated by the different sensorsand processes them to determine: (i) a pulse transit time calculatedusing a time difference between features in two separate time-dependentwaveforms, (ii) a blood pressure value calculated from the timedifference, and (iii) a motion parameter calculated from at least onemotion waveform. A wireless communication system, also worn on thepatient's body and connected to the processing component, transmits theblood pressure value and the motion parameter to a remote receiver.

The contractile property of the patient's heart, for example, can be abeat, expansion, contraction, or any time-dependent variation of theheart that launches both electrical signals and a bolus of blood in thepatient. The time-dependent waveform that results from this process, forexample, can be an ECG waveform measured from any vector on the patient,a PPG waveform, an acoustic waveform measured with a microphone, or apressure waveform measured with a transducer. In general, thesewaveforms can be measured from any location on the patient.

In embodiments, the first sensor is an optical sensor featuring a sourceof electromagnetic radiation configured to irradiate tissue of thepatient, and a detector configured to detect one or more properties ofthe electromagnetic radiation after it irradiates the tissue. Thissensor can detect, for example, an optical waveform that is indicativeof volumetric changes in the irradiated tissue caused by ventricularcontraction of the patient's heart. More specifically, the opticalwaveform represents a time-dependent change in optical absorption of anunderlying vessel resulting from the ejection of blood from the leftventricle. The second sensor can be a similar sensor, positioned onanother portion of the patient's body, and configured to measure asimilar, time-dependent waveform. In embodiments, the pulse transit timeused to determine blood pressure is a time difference between a peak ofa QRS complex of an ECG waveform and an inflection point in the opticalwaveform (corresponding, e.g., to a rising edge of the waveform).Alternatively, the transit time is determined from time-dependentfeatures on two separate optical waveforms.

The remote receiver that receives information describing the patient'sblood pressure values and motion is typically configured to generate analarm condition based on the blood pressure value, wherein the alarmcondition can be modified based on a motion parameter. For example, analarm condition indicating a need for medical intervention can bemodified (e.g., turned off) by a motion parameter indicating that thepatient is ambulatory.

Typically each of the three motion-detecting sensors is anaccelerometer. The motion-detecting sensor positioned on the forearm istypically worn on the wrist, proximate to the processing component. Themotion-detecting sensor positioned on the upper arm is typicallyelectrically connected to the processing component through a cable. Andthe third motion-detecting sensor is typically positioned on the chest,and is also electrically connected to the processing component throughthe same cable. When referring to the motion-detecting sensors,‘forearm’, as used herein, means any portion of the arm below the elbow,e.g. the forearm, wrist, hand, and fingers. ‘Upper arm’ means anyportion of the arm above and including the elbow, e.g. the bicep,shoulder, and armpit.

The motion waveform generated by the motion-detecting sensor on theupper arm is transmitted to the processing component through the cableas a first digital data stream. Similarly, the motion waveform generatedby the motion-detecting sensor positioned at a body location other thanthe forearm or upper arm is transmitted to the processing componentthrough the same cable as a second digital data stream, wherein theprocessing component can resolve the first and second digital datastreams. One of the time-dependent waveforms is an ECG waveform that istransmitted to the processing component through the cable as a thirddigital data stream that is separately resolvable from each of the firstand second digital data streams.

In embodiments, the cable features a terminal portion that includes: aconnector configured for reversible attachment of one or more ECGelectrodes; an ECG circuit that receives electrical signals from one ormore ECG electrodes and process them to determine an ECG waveform; andan analog-to-digital converter that converts the ECG waveforms into thethird digital data stream. The terminal portion can also include one ofthe motion-detecting sensors, and is typically positioned at a bodylocation other than the forearm or upper arm of the patient. Inembodiments, the terminal portion is attached to the patient's chest.

In another aspect, the invention features a cable system that measuresphysiologic signals and motion information from a patient. The cablesystem integrates with a first sensor configured to detect a firsttime-dependent physiological waveform indicative of one or morecontractile properties of the patient's heart, and includes: (i) anelectrical sensor featuring at least two electrodes connected to anelectrical circuit that generates a time-dependent electrical waveformindicative of contractile properties of the patient's heart, and (ii) atleast two motion-detecting sensors that each generate at least onetime-dependent motion waveform. Both the cable and the first sensorconnect to a processing component that performs functions similar tothat described above. Collectively, these systems can measure a bloodpressure based on PTT, as described above.

The cable features a transmission line connected to: (i) the electricalsensor configured to receive the time-dependent electrical waveform; and(ii) either one or two motion-detecting sensors configured to receive atime-dependent motion waveform. The motion-detecting sensors aretypically worn on the patient's upper arm, lower arm, and chest, withthese arm positions defined above. The cable additionally includes aterminal portion, similar to that described above, that features theelectrical circuit that determines the time-dependent electricalwaveform, at least one motion-detecting sensor that determines thetime-dependent motion waveform, and at least one analog-to-digitalconverter that digitizes these two waveforms before they pass throughthe cable. As described above, these waveforms are digitized to formseparate digital data streams that are separately resolvable by theprocessing component. The electrical circuit in the terminal portionconnects to at least three electrodes, with each electrode configured todetect an electrical signal from the patient's body. The electrodestypically connect to single-wire cables that plug into the terminalportion, and are typically worn on the patient's chest in a conventional‘Einthoven's Triangle’ configuration. In embodiments, the terminalportion is a removable component featuring an ECG circuit that, in itsdifferent configurations, supports three, five, and twelve-lead ECGsystems.

In another aspect, the invention features a method for calibrating aPTT-based blood pressure measurement using changes in a patient's armheight. The system includes a first sensor (e.g. an optical sensor) thatgenerates a first time-dependent waveform indicative of one or morecontractile properties of the patient's heart; and an electrical sensorthat generates a time-dependent electrical waveform indicative of one ormore contractile properties of the patient's heart. At least twomotion-detecting sensors positioned on separate locations on thepatient's body (e.g. the upper and lower arms) generate time-dependentmotion waveforms that can be processed to determine, e.g., arm height. Aprocessing component, similar to that described above, processes thetime-dependent waveforms to determine: (i) first and second armpositions calculated from one or more of the time-dependent motionwaveforms; (ii) first and second time differences between features inthe time-dependent waveforms acquired in, respectively, the first andsecond arm positions; (iii) first and second blood pressure valuescalculated, respectively, at the first and second arm positions; and(iv) a blood pressure factor calculated using the first blood pressurevalue and the first time difference, together with the second bloodpressure value and the second time difference.

In embodiments, the processing component further features a displaycomponent that renders a graphic interface which instructs the patientto move their arm to the first arm position and then to the second armposition. The processing component can also include an audio componentthat provides comparable audible instructions. Alternatively, theprocessing component analyzes the time-dependent motion waveforms toautomatically detect when the patient's arm is in the first and secondpositions. While in these positions it automatically determines thefirst and second time difference and the corresponding first and secondblood pressure values, and finally uses this information to determinethe blood pressure factor. Ideally information gathered from more thantwo arm positions is used to determine the blood pressure factor.

The blood pressure factor essentially represents a calibration for thePTT-based blood pressure measurement. Once it is determined, theprocessing component determines a third time difference from the twotime-dependent waveforms, and determines a blood pressure value usingthis time difference and the blood pressure factor.

In embodiments, the system additionally includes a pneumatic systemfeaturing an inflatable cuff, a pump, and a pressure sensor that,collectively, make an oscillometric blood pressure measurement. During acalibration measurement, the inflatable cuff attaches to the patient'sarm and is inflated by the pump. The pressure sensor measures atime-dependent pressure waveform representing a pressure within theinflatable cuff. Pulsations in the patient's arm caused by their bloodpressure are superimposed on the pressure waveform. This can be donewhile the cuff is inflating or deflating, with both these processesoccurring at a rate of 7-10 mmHg to make an accurate blood pressuremeasurement. Once this measurement is complete, the processing componentanalyzes the time-dependent pressure waveform to calculate at least onecuff-based blood pressure value, which it then uses along with a timedifference and the blood pressure factor to continuously determine thepatient's blood pressure during future, cuff-free measurements.

In another aspect, the invention provides a method for monitoring apatient featuring the following steps: (i) detecting first and secondtime-dependent physiological waveform indicative of one or morecontractile properties of the patient's heart with a first and secondsensors configured to be worn on the patient's body; (ii) detecting setsof time-dependent motion waveforms with at least two motion-detectingsensors positioned on different locations on the patient's body; (iii)processing the first and second time-dependent physiological waveformsto determine at least one vital sign; (iv) analyzing at least a portionof each set of time-dependent motion waveforms, or a mathematicalderivative thereof, to determine a motion parameter; (v) processing themotion parameter to determine a probability that the patient isundergoing a specific activity state; and (vi) estimating the patient'sactivity state based on the probability.

In embodiments, the method includes calculating a mathematical transform(e.g. a Fourier Transform) of at least one motion waveform to determinea frequency-dependent motion waveform (e.g. a power spectrum). Theamplitude of at least one frequency component of the power spectrum canthen be processed to determine the motion parameter. For example, a bandof frequency components between 0-3 Hz typically indicates that thepatient is walking, while a similar band between 0-10 Hz typicallyindicates that the patient is convulsing. A higher-frequency bandbetween 0-15 Hz typically indicates that a patient is falling. In thislast case, the time-dependent motion waveform typically includes asignature (e.g. a rapid change in value) that can be further processedto indicate falling. Typically this change represents at least a 50%change in the motion waveform's value within a time period of less than2 seconds.

Additionally, the analysis can include measuring a time-dependentvariation (e.g. a standard deviation or mathematical derivative) ofleast one motion waveform to determine the motion parameter. In otherembodiments, the method includes determining the motion parameter bycomparing a time-dependent motion waveform to a mathematical functionusing, e.g., a numerical fitting algorithm such as a linear leastsquares or Marquardt-Levenberg non-linear fitting algorithm.

In embodiments, the method further includes determining a ‘logitvariable’ z, or a mathematical derivative thereof, wherein z is definedas:z=b ₀ +b ₁ x ₁ +b ₂ x ₂ + . . . +b _(m) x _(m)and wherein b₀, b₁, b₂, and b_(m), are predetermined constants relatedto motion, and at least one of x₀, x₁, x₂, and x_(m), is a motionparameter determined from an amplitude of at least one frequencycomponent of the power spectrum or from a time-dependent waveform. Themethod then processes the logit variable z with a mathematical functionto determine the probability that the patient is undergoing a specificactivity state. For example, z can be processed to generate aprobability function P, or a mathematical derivative thereof, wherein Pis defined as:

$P = \frac{1}{1 - {\exp( {- z} )}}$and indicates a probability of an activity state. The method can thencompare P, or a mathematical derivative thereof, to a predeterminedthreshold value to estimate the patient's activity state.

In embodiments this method is used to estimate activity and posturestates such as resting, moving, sitting, standing, walking, running,falling, lying down, and convulsing. In embodiments, the vital signdetermined with this method is blood pressure calculated from a timedifference (e.g. a PTT value) between features in the ECG and PPGwaveforms, or alternatively using features between any combination oftime-dependent ECG, PPG, acoustic, or pressure waveforms. This includes,for example, two PPG waveforms measured from different locations on thepatient's body.

In another aspect the invention provides a method for monitoring vitalsigns and posture of a patient. A monitor, similar to that describedabove, measures vital signs from time-dependent waveforms (e.g. anycombination of optical, electrical, acoustic, or pressure waveforms) anda patient's posture with at least one motion-detecting sensor positionedon the patient's torso (e.g., an accelerometer positioned on thepatient's chest). The processing component analyzes at least a portionof a set of time-dependent motion waveforms generated by themotion-detecting sensor to determine a vector corresponding to motion ofthe patient's torso. It then compares the vector to a coordinate spacerepresentative of how the motion-detecting sensor is oriented on thepatient to determine a posture parameter (e.g. standing upright,sitting, and lying down).

To determine the vector the method includes an algorithm or computationfunction that analyzes three time-dependent motion waveforms, eachcorresponding to a unique axis of the motion-detecting sensor. Themotion waveforms can yield three positional vectors that define acoordinate space. In a preferred embodiment, for example, the firstpositional vector corresponds to a vertical axis, a second positionalvector corresponds to a horizontal axis, and the third positional vectorcorresponds to a normal axis extending normal from the patient's chest.Typically the posture parameter is an angle, e.g. an angle between thevector and at least one of the three positional vectors. For example,the angle can be between the vector and a vector corresponding to avertical axis. The patient's posture is estimated to be upright if theangle is less than a threshold value that is substantially equivalent to45 degrees (e.g., 45 degrees+/−10 degrees); otherwise, the patient'sposture is estimated to be lying down. If the patient is lying down, themethod can analyze the angle between the vector and a vectorcorresponding to a normal axis extending normal from the patient'schest. In this case, the patient's posture is estimated to be supine ifthe angle is less than a threshold value substantially equivalent to 35degrees (e.g., 35 degrees+/−10 degrees), and prone if the angle isgreater than a threshold value substantially equivalent to 135 degrees(e.g., 135 degrees+/−10 degrees). Finally, if the patient is lying down,the method can analyze the angle between the vector and a vectorcorresponding to a horizontal axis. In this case, the patient isestimated to be lying on a first side if the angle is less than athreshold value substantially equivalent to 90 degrees (e.g., 90degrees+/−10 degrees), and lying on an opposing side if the angle isgreater than a threshold value substantially equivalent to 90 degrees(e.g., 90 degrees+/−10 degrees).

Blood pressure is determined continuously and non-invasively using atechnique, based on PTT, which does not require any source for externalcalibration. This technique, referred to herein as the ‘compositetechnique’, is carried out with a body-worn vital sign monitor thatmeasures blood pressure and other vital signs, and wirelessly transmitsthem to a remote monitor. The composite technique is described in detailin the co-pending patent application entitled: VITAL SIGN MONITOR FORMEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, AND PRESSUREWAVEFORMS (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008), the contentsof which are fully incorporated herein by reference.

Still other embodiments are found in the following detailed descriptionof the invention, and in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic drawing of a body-worn vital sign monitorfeaturing three accelerometers for detecting motion, along with ECG,optical, and pneumatic systems for measuring vital signs;

FIG. 2 shows a graph of time-dependent waveforms (ECG, PPG, and ACC₁₋₉)generated by, respectively, the ECG system, the optical system, and theaccelerometer system of FIG. 1;

FIGS. 3A and 3B show an image of the body-worn vital sign monitor ofFIG. 1 attached to a patient with and without, respectively, acuff-based pneumatic system used for an indexing measurement;

FIG. 4 shows an image of the wrist-worn transceiver featured in thebody-worn vital sign monitor of FIGS. 3A and 3B;

FIG. 5 shows a schematic drawing of a coordinate system used tocalibrate accelerometers used in the body-worn vital sign monitor ofFIGS. 3A and 3B;

FIG. 6 shows a schematic drawing of the three accelerometers, attachedto a patient's arm and connected to the body-worn vital sign monitor ofFIGS. 3A, 3B, and 4;

FIG. 7 shows a schematic drawing of a coordinate system representing anaccelerometer coordinate space superimposed on a patient's torso;

FIG. 8 shows the accelerometer coordinate space of FIG. 7 and a vectorrepresenting the direction and magnitude of gravity, along with anglesseparating the vector from each axis of the coordinate system;

FIG. 9A is a graph showing time-dependent motion waveforms correspondingto different posture states and measured with an accelerometerpositioned on a patient's chest;

FIG. 9B is a graph showing posture states calculated using thetime-dependent motion waveforms of FIG. 9A and a mathematical model fordetermining a patient's posture;

FIG. 10 is a graph of time-dependent waveforms indicating a patient'selbow height and corresponding PTT;

FIGS. 11A and 11C are graphs of time-dependent waveforms generated withan accelerometer that represent, respectively, a patient walking andconvulsing;

FIGS. 11B and 11D are graphs of frequency-dependent waveformsrepresenting the power spectra of the time-dependent waveforms shown,respectively, in FIGS. 11A and 11C;

FIG. 12 is a schematic drawing of a calculation used to determine a typeof activity state exhibited by a patient;

FIGS. 13A and 13B are receiver operating characteristic (ROC) curvesthat show results from the calculation of FIG. 12 for, respectively, apatient that is walking and resting; and

FIG. 14 is a flow chart describing an algorithm that processesinformation from the body-worn vital sign monitor of FIGS. 3A, 3B, and 4to continuously determine a patient's vital signs and motion.

DETAILED DESCRIPTION OF THE INVENTION

System Overview

FIG. 1 shows a schematic drawing of a body-worn vital sign monitor 10according to the invention featuring a wrist-worn transceiver 12 thatcontinuously determines vital signs (e.g. SYS, DIA, SpO2, heart rate,respiratory rate, and temperature) and motion (e.g. posture, arm height,activity level, and degree of motion) for, e.g., an ambulatory patientin a hospital. The transceiver 12 connects to three separateaccelerometers 14 a, 14 b, 14 c distributed along a patient's arm andtorso and connected to a single cable. Each of these sensors measuresthree unique ACC waveforms, each corresponding to a separate axis (x, y,or z), which are digitized internally and sent to a computer processingunit (CPU) 22 within the transceiver 12 for processing. The transceiver12 also connects to an ECG system 16 that measures an ECG waveform, anoptical system 18 that measures a PPG waveform, and a pneumatic system20 for making cuff-based ‘indexing’ blood pressure measurementsaccording to the composite technique. Collectively, these systems 14a-c, 16, 18, and 20 continuously measure the patient's vital signs andmotion.

The ECG 16 and pneumatic 20 systems are stand-alone systems that includea separate microprocessor and analog-to-digital converter. During ameasurement, they connect to the transceiver 12 through connectors 28,30 and supply digital inputs using a communication protocol that runs ona controller-area network (CAN) bus. The CAN bus is a serial interface,typically used in the automotive industry, which allows differentelectronic systems to effectively communicate with each other, even inthe presence of electrically noisy environments. A third connector 32also supports the CAN bus and is used for ancillary medical devices(e.g. a glucometer) that is either worn by the patient or present intheir hospital room.

The optical system 18 features an LED and photodetector and, unlike theECG 16 and pneumatic 20 systems, generates an analog electrical signalthat connects through a cable 36 and connector 26 to the transceiver 12.As is described in detail below, the optical 18 and ECG 16 systemsgenerate synchronized time-dependent waveforms that are processed withthe composite technique to determine a PTT-based blood pressure alongwith motion information. The body-worn vital sign monitor 10 measuresthese parameters continuously and non-invasively characterize thehospitalized patient.

The first accelerometer 14 a is surface-mounted on a printed circuitedboard within the transceiver 12, which is typically worn on thepatient's wrist like a watch. The second 14 b accelerometer is typicallydisposed on the upper portion of the patient's arm and attaches to acable 40 that connects the ECG system 16 to the transceiver 12. Thethird accelerometer 14 c is typically worn on the patient's chestproximal to the ECG system 16. The second 14 b and third 14 caccelerometers integrate with the ECG system 16 into a single cable 40,as is described in more detail below, which extends from the patient'swrist to their chest and supplies digitized signals over the CAN bus. Intotal, the cable 40 connects to the ECG system 16, two accelerometers 14b, 14 c, and at least three ECG electrodes (shown in FIGS. 3A and 3B,and described in more detail below). The cable typically includes 5separate wires bundled together with a single protective cladding: thewires supply power and ground to the ECG system 16 and accelerometers 14b, 14 c, provide high signal and low signal transmission lines for datatransmitted over the CAN protocol, and provide a grounded electricalshield for each of these four wires. It is held in place by the ECGelectrodes, which are typically disposable and feature an adhesivebacking, and a series of bandaid-like disposable adhesive strips. Thissimplifies application of the system and reduces the number of sensingcomponents attached to the patient.

To determine posture, arm height, activity level, and degree of motion,the transceiver's CPU 22 processes signals from each accelerometer 14a-c with a series of algorithms, described in detail below. In total,the CPU can process nine unique, time-dependent signals (ACC₁₋₉)corresponding to the three axes measured by the three separateaccelerometers. Specifically, the algorithms determine parameters suchas the patient's posture (e.g., sitting, standing, walking, resting,convulsing, falling), the degree of motion, the specific orientation ofthe patient's arm and how this affects vital signs (particularly bloodpressure), and whether or not time-dependent signals measured by the ECG16, optical 18, or pneumatic 20 systems are corrupted by motion. Oncethis is complete, the transceiver 12 uses an internal wirelesstransmitter 24 to send information in a series of packets, as indicatedby arrow 34, to a central nursing station within a hospital. Thewireless transmitter 24 typically operates on a protocol based on 802.11and communicates with an existing network within the hospital. Thisinformation alerts a medical professional, such as a nurse or doctor, ifthe patient begins to decompensate. A server connected to the hospitalnetwork typically generates this alarm/alert once it receives thepatient's vital signs, motion parameters, ECG, PPG, and ACC waveforms,and information describing their posture, and compares these parametersto preprogrammed threshold values. As described in detail below, thisinformation, particularly vital signs and motion parameters, is closelycoupled together. Alarm conditions corresponding to mobile andstationary patients are typically different, as motion can corrupt theaccuracy of vital signs (e.g., by adding noise), and induce changes inthem (e.g., through acceleration of the patient's heart and respiratoryrates).

FIG. 2 shows time-dependent ECG 50, PPG 52, and ACC₁₋₉ 54 waveforms thatthe body-worn vital sign monitor continuously collects and analyzes todetermine the patient's vital signs and motion. The ECG waveform 50,generated by the ECG system and three electrodes attached to thepatient's chest, features a sharply peaked QRS complex 56. This complex56 marks the peak of ventricular depolarization and informally indicatesthe beginning of each cardiac cycle. For a normal rhythm it occurs foreach heartbeat. The optical system generates a PPG 52 using an infraredLED and matched photodetector incorporated into an optical sensor thatattaches to the base of the patient's thumb. A pulsatile feature 58 inthe PPG 52 follows the QRS complex 56, typically by about one to twohundred milliseconds, and indicates a volumetric expansion in arteriesand capillaries disposed underneath the optical sensor. The temporaldifference between the peak of the QRS complex 56 and the foot 60 of thepulsatile feature 58 in the PPG waveform is the PTT, which as describedin detail below is used to determine blood pressure according to thecomposite technique.

Each accelerometer generates three time-dependent ACC waveforms 54(ACC₁₋₉) that, collectively, indicate the patient's motion. Eachwaveform is digitized within the accelerometer using an internalanalog-to-digital circuit. In general, the frequency and magnitude ofchange in the shape of the ACC waveform indicate the type of motion thatthe patient is undergoing. For example, a typical waveform 54 features arelatively time-invariant component 53 indicating a period of time whenthe patient is relatively still, and a time-variant component 62 whenthe patient's activity level increases. As described in detail below, afrequency-dependent analysis of these components yields the type anddegree of patient motion. During operation, an algorithm running on theCPU within the wrist-worn transceiver operates an algorithm thatperforms this analysis so that the patient's activity level can becharacterized in real time.

FIGS. 3A and 3B show how the body-worn system 10 described with respectto FIG. 1 attaches to a patient 70. These figures show twoconfigurations of the system: FIG. 3A shows the system used during theindexing portion of the composite technique, and includes a pneumatic,cuff-based system 85, while FIG. 3B shows the system used for subsequentcontinuous monitoring of the patient featuring a non-invasive bloodpressure (cNIBP) measurement. The indexing measurement typically takesabout 60 seconds, and is typically performed once every 4 hours. Oncethe indexing measurement is complete the cuff-based system is typicallyremoved from the patient. The remainder of the time the system 10performs the cNIBP measurement.

The body-worn system 10 features a wrist-worn transceiver 72, describedin more detail in FIG. 5, featuring a touch panel interface 73 thatdisplays blood pressure values and other vital signs. A wrist strap 90affixes the transceiver 72 to the patient's wrist like a conventionalwristwatch. A cable 92 connects an optical sensor 94 that wraps aroundthe base of the patient's thumb to the transceiver 72. During ameasurement, the optical sensor 94 generates a time-dependent PPG,similar to the waveform 52 shown in FIG. 2, which is processed alongwith an ECG to measure blood pressure. PTT-based measurements made fromthe thumb yield excellent correlation to blood pressure measured with afemoral arterial line. This provides an accurate representation of bloodpressure in the central regions of the patient's body.

To determine ACC waveforms, such as the time-dependent waveform 54 shownin FIG. 2, the body-worn vital sign monitor 10 features three separateaccelerometers located at different portions on the patient's arm. Thefirst accelerometer is surface-mounted on a circuit board in thewrist-worn transceiver 72 and measures signals associated with movementof the patient's wrist. The second accelerometer is included in a smallbulkhead portion 96 included along the span of the cable 86. During ameasurement, a small piece of disposable tape, similar in size to aconventional bandaid, affixes the bulkhead portion 96 to the patient'sarm. In this way the bulkhead portion 96 serves two purposes: 1) itmeasures a time-dependent ACC waveform from the mid-portion of thepatient's arm, thereby allowing their posture and arm height to bedetermined as described in detail below; and 2) it secures the cable 86to the patient's arm to increase comfort and performance of thebody-worn vital sign monitor 10.

The cuff-based module 85 features a pneumatic system 76 that includes apump, valve, pressure fittings, pressure sensor, analog-to-digitalconverter, microcontroller, and rechargeable battery. During an indexingmeasurement, it inflates a disposable cuff 84 and performs twomeasurements according to the composite technique: 1) it performs aninflation-based measurement of oscillometry to determine values for SYS,DIA, and MAP; and 2) it determines a patient-specific relationshipbetween PTT and MAP. These measurements are performed according to thecomposite technique, and are described in detail in the above-referencedpatent application entitled: ‘VITAL SIGN MONITOR FOR MEASURING BLOODPRESSURE USING OPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS’ (U.S. Ser.No. 12/138,194; filed Jun. 12, 2008), the contents of which have beenpreviously incorporated herein by reference.

The cuff 84 within the cuff-based pneumatic system 85 is typicallydisposable and features an internal, airtight bladder that wraps aroundthe patient's bicep to deliver a uniform pressure field. During theindexing measurement, pressure values are digitized by the internalanalog-to-digital converter, and sent through a cable 86 according tothe CAN protocol, along with SYS, DIA, and MAP blood pressures, to thewrist-worn transceiver 72 for processing as described above. Once thecuff-based measurement is complete, the cuff-based module 85 is removedfrom the patient's arm and the cable 86 is disconnected from thewrist-worn transceiver 72. cNIBP is then determined using PTT, asdescribed in detail below.

To determine an ECG, similar to waveform 50 shown in FIG. 2, thebody-worn vital sign monitor 10 features a small-scale, three-lead ECGcircuit integrated directly into a bulkhead 74 that terminates an ECGcable 82. The ECG circuit features an integrated circuit that collectselectrical signals from three chest-worn ECG electrodes 78 a-c connectedthrough cables 80 a-c. The ECG electrodes 78 a-c are typically disposedin a conventional ‘Einthoven's Triangle’ configuration which is atriangle-like orientation of the electrodes 78 a-c on the patient'schest that features three unique ECG vectors. From these electricalsignals the ECG circuit determines up to three ECG waveforms, which aredigitized using an analog-to-digital converter mounted proximal to theECG circuit, and sent through a cable 82 to the wrist-worn transceiver72 according to the CAN protocol. There, the ECG is processed with thePPG to determine the patient's blood pressure. Heart rate andrespiratory rate are determined directly from the ECG waveform usingknown algorithms, such as those described in the following reference,the contents of which are incorporated herein by reference: ‘ECG BeatDetection Using Filter Banks’, Afonso et al., IEEE Trans. Biomed Eng.,46:192-202 (1999). The cable bulkhead 74 also includes an accelerometerthat measures motion associated with the patient's chest as describedabove.

There are several advantages of digitizing ECG and ACC waveforms priorto transmitting them through the cable 82. First, a single transmissionline in the cable 82 can transmit multiple digital waveforms, eachgenerated by different sensors. This includes multiple ECG waveforms(corresponding, e.g., to vectors associated with three, five, andtwelve-lead ECG systems) from the ECG circuit mounted in the bulkhead74, along with waveforms associated with the x, y, and z axes ofaccelerometers mounted in the bulkheads 75, 96. Limiting thetransmission line to a single cable reduces the number of wires attachedto the patient, thereby decreasing the weight and cable-related clutterof the body-worn monitor. Second, cable motion induced by an ambulatorypatient can change the electrical properties (e.g. electricalimpendence) of its internal wires. This, in turn, can add noise to ananalog signal and ultimately the vital sign calculated from it. Adigital signal, in contrast, is relatively immune to such motion-inducedartifacts.

More sophisticated ECG circuits can plug into the wrist-worn transceiverto replace the three-lead system shown in FIGS. 3A and 3B. These ECGcircuits can include, e.g., five and twelve leads.

FIG. 4 shows a close-up view of the wrist-worn transceiver 72. Asdescribed above, it attaches to the patient's wrist using a flexiblestrap 90 which threads through two D-ring openings in a plastic housing106. The transceiver 72 features a touchpanel display 100 that renders agraphical user interface 73 which is altered depending on the viewer(typically the patient or a medical professional). Specifically, thetransceiver 72 includes a small-scale infrared barcode scanner 102 that,during use, can scan a barcode worn on a badge of a medicalprofessional. The barcode indicates to the transceiver's software that,for example, a nurse or doctor is viewing the user interface. Inresponse, the user interface 73 displays vital sign data and othermedical diagnostic information appropriate for medical professionals.Using this interface 73, the nurse or doctor, for example, can view thevital sign information, set alarm parameters, and enter informationabout the patient (e.g. their demographic information, medication, ormedical condition). The nurse can press a button on the user interface73 indicating that these operations are complete. At this point, thedisplay 100 renders an interface that is more appropriate to thepatient, e.g. it displays parameters similar to those from aconventional wristwatch, such as time of day and battery power.

As described above, the transceiver 72 features three CAN connectors 104a-c on the side of its upper portion, each which supports the CANprotocol and wiring schematics, and relays digitized data to theinternal CPU. Digital signals that pass through the CAN connectorsinclude a header that indicates the specific signal (e.g. ECG, ACC, orpressure waveform from the cuff-based module) and the sensor from whichthe signal originated. This allows the CPU to easily interpret signalsthat arrive through the CAN connectors 104 a-c, and means that theseconnectors are not associated with a specific cable. Any cableconnecting to the transceiver can be plugged into any connector 104 a-c.As shown in FIG. 3A, the first connector 104 a receives the cable 82that transports a digitized ECG waveform determined from the ECG circuitand electrodes, and digitized ACC waveforms measured by accelerometersin the cable bulkhead 74 and the bulkhead portion 96 associated with theECG cable 82.

The second CAN connector 104 b shown in FIG. 4 receives the cable 86that connects to the pneumatic cuff-based system 85 and used for thepressure-dependent indexing measurement. This connector is used toreceive a time-dependent pressure waveform delivered by the pneumaticsystem 85 to the patient's arm, along with values for SYS, DIA, and MAPvalues determined during the indexing measurement. A user unplugs thecable 86 from the connector 104 b once the indexing measurement iscomplete, and plugs it back in after approximately four hours foranother indexing measurement.

The final CAN connector 104 c can be used for an ancillary device, e.g.a glucometer, infusion pump, body-worn insulin pump, ventilator, orend-tidal CO₂ delivery system. As described above, digital informationgenerated by these systems will include a header that indicates theirorigin so that the CPU can process them accordingly.

The transceiver includes a speaker 101 that allows a medicalprofessional to communicate with the patient using a voice over Internetprotocol (VoIP). For example, using the speaker 101 the medicalprofessional could query the patient from a central nursing station ormobile phone connected to a wireless, Internet-based network within thehospital. Or the medical professional could wear a separate transceiversimilar to the shown in FIG. 4, and use this as a communication device.In this application, the transceiver 72 worn by the patient functionsmuch like a conventional cellular telephone or ‘walkie talkie’: it canbe used for voice communications with the medical professional and canadditionally relay information describing the patient's vital signs andmotion.

Algorithms for Determining Patient Motion, Posture, Arm Height, ActivityLevel and the Effect of these Properties on Blood Pressure

Described below is an algorithm for using the three accelerometersfeatured in the above-described body-worn vital sign monitor tocalculate a patient's motion, posture, arm height, activity level. Eachof these parameters affects both blood pressure and PTT, and thusinclusion of them in an algorithm can improve the accuracy of thesemeasurements and the alarms and calibration procedures associated withthem.

Calculating Arm Height

To calculate a patient's arm height it is necessary to build amathematical model representing the geometric orientation of thepatient's arm, as detected with signals from the three accelerometers.FIG. 5 shows a schematic image of a coordinate system 109 centeredaround a plane 110 used to build this model for determining patientmotion and activity level, and arm height. Each of these parameters, asdiscussed in detail below, has an impact on the patient's vital signs,and particularly blood pressure.

The algorithm for estimating a patient's motion and activity levelbegins with a calculation to determine their arm height. This is doneusing signals from accelerometers attached to the patient's bicep (i.e.,with reference to FIG. 3A, an accelerometer included in the bulkheadportion 96 of cable 86) and wrist (i.e. the accelerometersurface-mounted to the circuit board within the wrist-worn transceiver72). The mathematical model used for this algorithm features acalibration procedure used to identify the alignment of an axisassociated with a vector R_(A), which extends along the patient's arm.Typically this is done by assuming the body-worn vital sign monitor isattached to the patient's arm in a consistent manner, i.e. that shown inFIGS. 3A and 3B, and by using preprogrammed constants stored in memoryassociated with the CPU. Alternatively this can be done by prompting thepatient (using, e.g., the wrist-worn transceiver 72) to assume a knownand consistent position with respect to gravity (e.g., hanging their armdown in a vertical configuration). The axis of their arm is determinedby sampling a DC portion of time-dependent ACC waveforms along the x, y,and z axes associated with the two above-mentioned accelerometers (i.e.ACC₁₋₆; the resultant values have units of g's) during the calibrationprocedure, and storing these numerical values as a vector in memoryaccessible with the CPU within the wrist-worn transceiver.

The algorithm determines a gravitational vector R_(GA) at a later timeby again sampling DC portions of ACC₁₋₆. Once this is complete, thealgorithm determines the angle_(GA) between the fixed arm vector R_(A)and the gravitational vector R_(GA) by calculating a dot product of thetwo vectors. As the patient moves their arm, signals measured by the twoaccelerometers vary, and are analyzed to determine a change in thegravitational vector R_(GA) and, subsequently, a change in the angle_(GA). The angle _(GA) can then be combined with an assumed, approximatelength of the patient's arm (typically 0.8 m) to determine its heightrelative to a proximal joint, e.g. the elbow.

FIG. 6 indicates how this model and approach can be extended todetermine the relative heights of the upper 117 and lower 116 segmentsof a patient's arm 115. In this derivation, described below, i, j, krepresent the vector directions of, respectively, the x, y, and z axesof the coordinate system 109 shown in FIG. 5. Three accelerometers 102a-c are disposed, respectively, on the patient's chest just above theirarmpit, near their bicep, and near their wrist; this is consistent withpositioning within the body-worn vital sign monitor, as described inFIGS. 3A and 3B. The vector R_(B) extending along the upper portion 117of the patient's arm is defined in this coordinate system as:

_(B) =r _(bx) î+r _(By) ĵ+r _(Bz) {circumflex over (k)}  (1)At any given time, the gravitational vector R_(GB) is determined fromACC waveforms (ACC₁₋₃) using signals from the accelerometer 102 blocated near the patient's bicep, and is represented by equation (2)below:

_(GB) [n]=y _(Bx) [n]î+y _(By) [n]ĵ+y _(Bz) [n]{circumflex over(k)}  (2)Specifically, the CPU in the wrist-worn transceiver receives digitizedsignals representing the DC portion (e.g. component 53 in FIG. 2) of theACC₁₋₃ signals measured with accelerometer 102 b, as represented byequation (3) below, where the parameter n is the value (having units ofg's) sampled directly from the DC portion of the ACC waveform:y _(Bx) [n]=y _(DC,Bicep,x) [n]; y _(By) [n]=y _(DC,Bicep,y) [n]; y_(Bz) [n]=y _(DC,Bicep,z) [n]  (3)The cosine of the angle _(GB) separating the vector R_(B) and thegravitational vector R_(GB) is determined using equation (4):

$\begin{matrix}{{\cos( {\theta_{GB}\lbrack n\rbrack} )} = \frac{{{\overset{harpoonup}{R}}_{GB}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{B}}{{{{\overset{harpoonup}{R}}_{GB}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{B}}}} & (4)\end{matrix}$The definition of the dot product of the two vectors R_(B) and R_(GB)is:

_(GB) [n]·

_(B)=(y _(Bx) [n]×r _(Bx))+(y _(By) [n]×r _(By))+(y _(Bz) [n]×r_(Bz))  (5)and the definitions of the norms or magnitude of the vectors R_(B) andR_(GB) are:∥

_(GB) [n]∥√{square root over ((y _(Bx) [n])²+(y _(By) [n])²+(y _(Bz)[n])²)}{square root over ((y _(Bx) [n])²+(y _(By) [n])²+(y _(Bz)[n])²)}{square root over ((y _(Bx) [n])²+(y _(By) [n])²+(y _(Bz)[n])²)}  (6)and∥

_(B)∥=√{square root over ((r _(Bx))²+(r _(By))²+(r _(Bz))²)}{square rootover ((r _(Bx))²+(r _(By))²+(r _(Bz))²)}{square root over ((r _(Bx))²+(r_(By))²+(r _(Bz))²)}  (7)Using the norm values for these vectors and the angle _(GB) separatingthem, as defined in equation (4), the height of the patient's elbowrelative to their shoulder joint, as characterized by the accelerometeron their chest (h_(E)), is determined using equation (8), where thelength of the upper arm is estimated as L_(B):h _(E) [n]=−L _(B)×cos(θ_(GB) [n])  (8)As is described in more detail below, equation (8) estimates the heightof the patient's arm relative to their heart. And this, in turn, can beused to further improve the accuracy of PTT-based blood pressuremeasurements.

The height of the patient's wrist joint h_(w) is calculated in a similarmanner using DC components from the time-domain waveforms (ACC₄₋₆)collected from the accelerometer 102 a mounted within the wrist-worntransceiver. Specifically, the wrist vector R_(W) is given by equation(9):

_(W) =r _(Wx) î+r _(Wy) ĵ+r _(Wz) {circumflex over (k)}  (9)and the corresponding gravitational vector R_(GW) is given by equation(10):

_(GW) [n]=y _(Wx) [n]î+y _(Wy) [n]ĵ+y _(Wz) [n]{circumflex over(k)}  (10)The specific values used in equation (10) are measured directly from theaccelerometer 102 a; they are represented as n and have units of g's, asdefined below:y _(Wx) [n]=y _(DC,Wrist,x) [n]; y _(Wy) [n]=y _(DC,Wrist,y) [n]; y_(Wz) [n]=y _(DC,Wrist,z) [n]  (11)The vectors R_(W) and R_(GW) described above are used to determine thecosine of the angle _(GW) separating them using equation (12):

$\begin{matrix}{{\cos( {\theta_{GW}\lbrack n\rbrack} )} = \frac{{{\overset{harpoonup}{R}}_{GW}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{W}}{{{{\overset{harpoonup}{R}}_{WB}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{W}}}} & (12)\end{matrix}$The definition of the dot product between the vectors R_(W) and R_(GW)is:

_(GW) [n]·

_(W)=(y _(Wx) [n]×r _(Wx))+(y _(Wy) [n]×r _(Wy))+(y _(Wz) [n]×r_(Wz))  (13)and the definitions of the norm or magnitude of both the vectors R_(W)and R_(GW) are:∥

_(GW) [n]∥=√{square root over ((y _(Wx) [n])²+(y _(Wy) [n])²+(y _(Wz)[n])²)}{square root over ((y _(Wx) [n])²+(y _(Wy) [n])²+(y _(Wz)[n])²)}{square root over ((y _(Wx) [n])²+(y _(Wy) [n])²+(y _(Wz)[n])²)}  (14)and∥

_(W)∥=√{square root over ((r _(Wx))²+(r _(Wy))²+(r _(Wz))²)}{square rootover ((r _(Wx))²+(r _(Wy))²+(r _(Wz))²)}{square root over ((r _(Wx))²+(r_(Wy))²+(r _(Wz))²)}  (15)The height of the patient's wrist h_(W) can be calculated using the normvalues described above in equations (14) and (15), the cosine valuedescribed in equation (12), and the height of the patient's elbowdetermined in equation (8):h _(W) [n]=h _(E) [n]−L _(W)×cos(θ_(GW) [n])  (16)In summary, the algorithm can use digitized signals from theaccelerometers mounted on the patient's bicep and wrist, along withequations (8) and (16), to accurately determine the patient's arm heightand position. As described below, these parameters can then be used tocorrect the PTT and provide a blood pressure calibration, similar to thecuff-based indexing measurement described above, that can furtherimprove the accuracy of this measurement.Calculating the Influence of Arm Height on Blood Pressure

A patient's blood pressure, as measured near the brachial artery, willvary with their arm height due to hydrostatic forces and gravity. Thisrelationship between arm height and blood pressure enables twomeasurements: 1) a blood pressure ‘correction factor’, determined fromslight changes in the patient's arm height, can be calculated and usedto improve accuracy of the base blood pressure measurement; and 2) therelationship between PTT and blood pressure can be determined (like itis currently done using the indexing measurement) by measuring PTT atdifferent arm heights, and calculating the change in PTT correspondingto the resultant change in height-dependent blood pressure.Specifically, using equations (8) and (16) above, and (21) below, analgorithm can calculate a change in a patient's blood pressure (BP)simply by using data from two accelerometers disposed on the wrist andbicep. The BP can be used as the correction factor. Exact blood pressurevalues can be estimated directly from arm height using an initial bloodpressure value (determined, e.g., using the cuff-based module during aninitial indexing measurement), the relative change in arm height, andthe correction factor. This measurement can be performed, for example,when the patient is first admitted to the hospital. PTT determined atdifferent arm heights provides multiple data points, each correspondingto a unique pair of blood pressure values determined as described above.The change in PTT values (PTT) corresponds to changes in arm height.

From these data, the algorithm can calculate for each patient how bloodpressure changes with PTT, i.e. BP/PTT. This relationship relates tofeatures of the patient's cardiovascular system, and will evolve overtime due to changes, e.g., in the patient's arterial tone and vascularcompliance. Accuracy of the body-worn vital sign monitor's bloodpressure measurement can therefore be improved by periodicallycalculating BP/PTT. This is best done by: 1) combining a cuff-basedinitial indexing measurement to set baseline values for SYS, DIA, andMAP, and then determining BP/PTT as described above; and 2) continuallycalculating BP/PTT by using the patient's natural motion, oralternatively using well-defined motions (e.g., raising and lower thearm to specific positions) as prompted at specific times by monitor'suser interface.

Going forward, the body-worn vital sign monitor measures PTT, and canuse this value and the relationship determined from the above-describedcalibration to convert this to blood pressure. All future indexingmeasurements can be performed on command (e.g., using audio or visualinstructions delivered by the wrist-worn transceiver) using changes inarm height, or as the patient naturally raises and lowers their arm asthey move about the hospital.

To determine the relationship between PTT, arm height, and bloodpressure, the algorithm running on the wrist-worn transceiver is derivedfrom a standard linear model shown in equation (17):

$\begin{matrix}{{PTT} = {{( \frac{1}{m_{BP}} ) \times P_{MAP}} + \overset{\sim}{B}}} & (17)\end{matrix}$Assuming a constant velocity of the arterial pulse along an arterialpathway (e.g., the pathway extending from the heart, through the arm, tothe base of the thumb):

$\begin{matrix}{\frac{\partial({PWV})}{\partial r} = 0} & (18)\end{matrix}$the linear PTT model described in equation (17) becomes:

$\begin{matrix}{\frac{\partial({PTT})}{\partial r} = {( \frac{1}{L} )( {{\frac{1}{m_{BP}} \times {MAP}} + \overset{\sim}{B}} )}} & (19)\end{matrix}$Equation (19) can be solved using piecewise integration along the upper117 and lower 116 segments of the arm to yield the following equationfor height-dependent PTT:

$\begin{matrix}{{PTT} = {( {{\frac{1}{m_{BP}} \times {MAP}} + B} ) - {\frac{1}{m_{BP}} \times \begin{bmatrix}{{( \frac{L_{1}}{L} )( \frac{\rho\;{Gh}_{E}}{2} )} +} \\{( \frac{L_{2}}{L} )( {\frac{\rho\; G}{2}( {h_{W} + h_{E}} )} )}\end{bmatrix}}}} & (20)\end{matrix}$From equation (20) it is possible to determine a relative pressurechange P_(rel) induced in a cNIBP measurement using the height of thepatient's wrist (h_(W)) and elbow (h_(E)):

$\begin{matrix}{{P_{rel}\lbrack n\rbrack} = {{( \frac{L_{1}}{L} )( \frac{\rho\;{{Gh}_{E}\lbrack n\rbrack}}{2} )} + {( \frac{L_{2}}{L} )( {\frac{\rho\; G}{2}( {{h_{W}\lbrack n\rbrack} + {h_{E}\lbrack n\rbrack}} )} )}}} & (21)\end{matrix}$As described above, P_(rel) can be used to both calibrate the cNIBPmeasurement deployed by the body-worn vital sign monitor, or supply aheight-dependent correction factor that reduces or eliminates the effectof posture and arm height on a PTT-based blood pressure measurement.

FIG. 10 shows actual experimental data that illustrate how PTT changeswith arm height. Data for this experiment were collected as the subjectperiodically raised and lowered their arm using a body-worn vital signmonitor similar to that shown in FIGS. 3A and 3B. Such motion wouldoccur, for example, if the patient was walking. As shown in FIG. 10,changes in the patient's elbow height are represented by time-dependentchanges in the DC portion of an ACC waveform, indicated by trace 160.These data are measured directly from an accelerometer positioned nearthe patient's bicep, as described above. PTT is measured from the samearm using the PPG and ECG waveforms, and is indicated by trace 162. Asthe patient raises and lowers their arm their PTT rises and fallsaccordingly, albeit with some delay due to the reaction time of thepatient's cardiovascular system.

Calculating a Patient's Posture

As described above, a patient's posture can influence how theabove-described system generates alarms/alerts. The body-worn monitorcan determine a patient's posture using time-dependent ACC waveformscontinuously generated from the three patient-worn accelerometers, asshown in FIGS. 3A, B. In embodiments, the accelerometer worn on thepatient's chest can be exclusively used to simplify this calculation. Analgorithm operating on the wrist-worn transceiver extracts DC valuesfrom waveforms measured from this accelerometer and processes them withan algorithm described below to determine posture. Specifically,referring to FIG. 7, torso posture is determined for a patient 145 usingangles determined between the measured gravitational vector and the axesof a torso coordinate space 146. The axes of this space 146 are definedin a three-dimensional Euclidean space where

_(CV) is the vertical axis,

_(CH) is the horizontal axis, and

R_(CN) is the normal axis. These axes must be identified relative to a‘chest accelerometer coordinate space’ before the patient's posture canbe determined.

The first step in this procedure is to identify alignment of

_(CV) in the chest accelerometer coordinate space. This can bedetermined in either of two approaches. In the first approach,

_(CV) is assumed based on a typical alignment of the body-worn monitorrelative to the patient. During manufacturing, these parameters are thenpreprogrammed into firmware operating on the wrist-worn transceiver. Inthis procedure it is assumed that accelerometers within the body-wornmonitor are applied to each patient with essentially the sameconfiguration. In the second approach,

_(CV) is identified on a patient-specific basis. Here, an algorithmoperating on the wrist-worn transceiver prompts the patient (using,e.g., video instruction operating on the display, or audio instructionstransmitted through the speaker) to assume a known position with respectto gravity (e.g., standing up with arms pointed straight down). Thealgorithm then calculates

_(CV) from DC values corresponding to the x, y, and z axes of the chestaccelerometer while the patient is in this position. This case, however,still requires knowledge of which arm (left or right) the monitor isworn on, as the chest accelerometer coordinate space can be rotated by180 degrees depending on this orientation. A medical professionalapplying the monitor can enter this information using the GUI, describedabove. This potential for dual-arm attachment requires a set of twopre-determined vertical and normal vectors which are interchangeabledepending on the monitor's location. Instead of manually entering thisinformation, the arm on which the monitor is worn can be easilydetermined following attachment using measured values from the chestaccelerometer values, with the assumption that

_(CV) is not orthogonal to the gravity vector.

The second step in the procedure is to identify the alignment of

_(CN) in the chest accelerometer coordinate space. The monitor candetermine this vector, similar to the way it determines

_(CV), with one of two approaches. In the first approach the monitorassumes a typical alignment of the chest-worn accelerometer on thepatient. In the second approach, the alignment is identified byprompting the patient to assume a known position with respect togravity. The monitor then calculates

_(CN) from the DC values of the time-dependent ACC waveform.

The third step in the procedure is to identify the alignment of

_(CH) in the chest accelerometer coordinate space. This vector istypically determined from the vector cross product of

_(CV) and

_(CN), or it can be assumed based on the typical alignment of theaccelerometer on the patient, as described above.

FIG. 8 shows the geometrical relationship between

_(CV) 140,

_(CN) 141, and

_(CH) 142 and a gravitational vector

_(G) 143 measured from a moving patient in a chest accelerometercoordinate space 139. The body-worn monitor continually determines apatient's posture from the angles separating these vectors.Specifically, the monitor continually calculates

_(G) 143 for the patient using DC values from the ACC waveform measuredby the chest accelerometer. From this vector, the body-worn monitoridentifies angles (θ_(VG), θ_(NG), and θ_(HG)) separating it from

_(CV) 140,

_(CN) 141, and

_(CH) 142. The body-worn monitor then compares these three angles to aset of predetermine posture thresholds to classify the patient'sposture.

The derivation of this algorithm is as follows. Based on either anassumed orientation or a patient-specific calibration proceduredescribed above, the alignment of

_(CV) in the chest accelerometer coordinate space is given by:

_(CV) =r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflex over (k)}  (22)At any given moment,

_(G) is constructed from DC values of the ACC waveform from the chestaccelerometer along the x, y, and z axes:

_(G) [n]=y _(Cx) [n]î+y _(Cy) [n]ĵ+y _(Cz) [n]{circumflex over(k)}  (23)Equation (24) shows specific components of the ACC waveform used forthis calculation:y _(Cx) [n]=y _(DC,chest,x) [n]; y _(Cy) [n]=y _(DC,chest,y) [n]; y_(Cz) [n]=y _(DC,chest,z) [n]  (24)The angle between

_(CV) and

_(G) is given by equation (25):

$\begin{matrix}{{\theta_{VG}\lbrack n\rbrack} = {\arccos( \frac{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{CV}}{{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{CV}}} )}} & (25)\end{matrix}$where the dot product of the two vectors is defined as:

_(G) [n]·

_(CV)=(y _(Cx) [n]×r _(CVx))+(y _(Cy) [n]×r _(CVy))+(y _(Cz) [n]×r_(CVz))  (26)The definition of the norms of

_(G) and

_(CV) are given by equations (27) and (28):∥

_(G) [n]∥=√{square root over ((y _(Cx) [n])²+(y _(Cy) [n])²+(y _(Cz)[n])²)}{square root over ((y _(Cx) [n])²+(y _(Cy) [n])²+(y _(Cz)[n])²)}{square root over ((y _(Cx) [n])²+(y _(Cy) [n])²+(y _(Cz)[n])²)}  (27)∥

_(CV)∥=√{square root over ((r _(CVx))²+(r _(CVy))²+(r _(CVz))²)}{squareroot over ((r _(CVx))²+(r _(CVy))²+(r _(CVz))²)}{square root over ((r_(CVx))²+(r _(CVy))²+(r _(CVz))²)}  (28)

As shown in equation (29), the monitor compares the vertical angleθ_(VG) to a threshold angle to determine whether the patient is vertical(i.e. standing upright) or lying down:if θ_(VG)≦45° then Torso State=0, the patient is upright  (29)If the condition in equation (29) is met the patient is assumed to beupright, and their torso state, which is a numerical value equated tothe patient's posture, is equal to 0. The torso state is processed bythe body-worn monitor to indicate, e.g., a specific icon correspondingto this state. The patient is assumed to be lying down if the conditionin equation (8) is not met, i.e. θ_(VG)>45 degrees. Their lying positionis then determined from angles separating the two remaining vectors, asdefined below.

The angle θ_(NG) between

_(CN) and

_(G) determines if the patient is lying in the supine position (chestup), prone position (chest down), or on their side. Based on either anassumed orientation or a patient-specific calibration procedure, asdescribed above, the alignment of

_(CN) is given by equation (30), where i, j, k represent the unitvectors of the x, y, and z axes of the chest accelerometer coordinatespace respectively:

_(CN) =r _(CNx) î+r _(CNy) ĵ+r _(CNz) {circumflex over (k)}  (30)The angle between

_(CN) and

_(G) determined from DC values extracted from the chest accelerometerACC waveform is given by equation (31):

$\begin{matrix}{{\theta_{NG}\lbrack n\rbrack} = {\arccos( \frac{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{CN}}{{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{CN}}} )}} & (31)\end{matrix}$The body-worn monitor determines the normal angle θ_(NG) and thencompares it to a set of predetermined threshold angles to determinewhich position the patient is lying in, as shown in equation (32):if θ_(NG)≦35° then Torso State=1, the patient is supineif θ_(NG)≧135° then Torso State=2, the patient is prone  (32)If the conditions in equation (32) are not met then the patient isassumed to be lying on their side. Whether they are lying on their rightor left side is determined from the angle calculated between thehorizontal torso vector and measured gravitational vectors, as describedabove.

The alignment of

_(CH) is determined using either an assumed orientation, or from thevector cross-product of

_(CV) and

_(CN) as given by equation (33), where i, j, k represent the unitvectors of the x, y, and z axes of the accelerometer coordinate spacerespectively. Note that the orientation of the calculated vector isdependent on the order of the vectors in the operation. The order belowdefines the horizontal axis as positive towards the right side of thepatient's body.

_(CH) =r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflex over (k)}=

_(CV) ×

_(CN)  (33)The angle θ_(HG) between

_(CH) and

_(G) is determined using equation (34):

$\begin{matrix}{{\theta_{HG}\lbrack n\rbrack} = {\arccos( \frac{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{harpoonup}{R}}_{CH}}{{{{\overset{harpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{harpoonup}{R}}_{CH}}} )}} & (34)\end{matrix}$The monitor compares this angle to a set of predetermined thresholdangles to determine if the patient is lying on their right or left side,as given by equation (35):if θ_(HG)≧90° then Torso State=3, the patient is on their right sideif θ_(NG)<90° then Torso State=4, the patient is on their leftside  (35)Table 1 describes each of the above-described postures, along with acorresponding numerical torso state used to render, e.g., a particularicon:

TABLE 1 postures and their corresponding torso states Posture TorsoState Upright 0 supine: lying on back 1 prone: lying on chest 2 lying onright side 3 lying on left side 4 undetermined posture 5

FIGS. 9A and 9B show, respectively, graphs of time-dependent ACCwaveforms 150 measured along the x, y, and z-axes, and the torso states(i.e. postures) 151 determined from these waveforms for a movingpatient. As the patient moves, the DC values of the ACC waveformsmeasured by the chest accelerometer vary accordingly, as shown by thegraph 150 in FIG. 9A. The body-worn monitor processes these values asdescribed above to continually determine

_(G) and the various quantized torso states for the patient, as shown inthe graph 151 in FIG. 9B. The torso states yield the patient's postureas defined in Table 1. For this study the patient rapidly alternatedbetween standing, lying on their back, chest, right side, and left sidewithin about 150 seconds. As described above, different alarm/alertconditions (e.g. threshold values) for vital signs can be assigned toeach of these postures, or the specific posture itself may result in analarm/alert. Additionally, the time-dependent properties of the graph151 can be analyzed (e.g. changes in the torso states can be counted) todetermine, for example, how often the patient moves in their hospitalbed. This number can then be equated to various metrics, such as a ‘bedsore index’ indicating a patient that is so stationary in their bed thatlesions may result. Such a state could then be used to trigger analarm/alert to the supervising medical professional.

Calculating a Patient's Activity

An algorithm can process information generated by the accelerometersdescribed above to determine a patient's specific activity (e.g.,walking, resting, convulsing), which is then used to reduce theoccurrence of false alarms. This classification is done using a‘logistic regression model classifier’, which is a type of classifierthat processes continuous data values and maps them to an output thatlies on the interval between 0 and 1. A classification ‘threshold’ isthen set as a fractional value within this interval. If the model outputis greater than or equal to this threshold, the classification isdeclared ‘true’, and a specific activity state can be assumed for thepatient. If the model output falls below the threshold, then thespecific activity is assumed not to take place.

This type of classification model offers several advantages. First, itprovides the ability to combine multiple input variables into a singlemodel, and map them to a single probability ranging between 0 and 1.Second, the threshold that allows the maximum true positive outcomes andthe minimum false positive outcomes can be easily determined from a ROCcurve, which in turn can be determined using empirical experimentationand data. Third, this technique requires minimal computation.

The formula for the logistic regression model is given by equation (36)and is used to determine the outcome, P, for a set of buffered data:

$\begin{matrix}{P = \frac{1}{1 - {\exp( {- z} )}}} & (36)\end{matrix}$The logit variable z is defined in terms of a series of predictors(x_(i)), each affected by a specific type of activity, and determined bythe three accelerometers worn by the patient, as shown in equation (37):z=b ₀ +b ₁ x ₁ +b ₂ x ₂ + . . . +b _(m) x _(m)  (37)In this model, the regression coefficients (b_(i), i=0, 1, . . . , m)and the threshold (P_(th)) used in the patient motion classifier andsignal corruption classifiers are determined empirically from datacollected on actual subjects. The classifier results in a positiveoutcome as given in equation (38) if the logistic model output, P, isgreater than the predetermined threshold, P_(th):If P≧P _(th) then Classifier State=1  (38)

FIGS. 11A-D indicate how the above-described predictors can be processedto determine a patient's specific activity level. As shown in FIG. 11A,a time-dependent ACC waveform 130 measured with an accelerometer from awalking patient typically features DC portions before 129 a and after129 b the patient finishes walking, and a periodic AC portion 131measured during walking. A Fourier Transform of the waveform, as shownby the frequency-dependent waveform 132 in FIG. 11B, yields a powerspectrum featuring a well-defined frequency component 133 (typicallynear 1-3 Hz, with an average of about 1.5 Hz), corresponding to thefrequency of the patient's stride. FIGS. 11C and 11D indicate anotheractivity state. Here, the patient is convulsing, and the time-dependentACC waveform 134 shown in FIG. 11C features two oscillating ‘bursts’ 135a, 135 b separated by periods of inactivity. A Fourier Transform of thiswaveform, as shown by the power spectrum 136 in FIG. 11D, indicates twofrequency components 137 a, 137 b, both having relatively large values(typically 4-8 Hz, depending on the type of convulsion) compared to thatshown in FIG. 11B. These frequency components 137 a, 137 b correspond tothe frequency-dependent content of the oscillating bursts 135 a, 135 b.

FIG. 12 shows a block diagram 180 indicating the mathematical model usedto determine the above-described logistic regression model classifier.In this model, the series of predictor variables (x_(i)) are determinedfrom statistical properties of the time-dependent ACC waveforms, alongwith specific frequency components contained in the power spectra ofthese waveforms. The frequency components are determined in alow-frequency region (0-20 Hz) of these spectra that corresponds tohuman motion. Specifically, the predictor variables can be categorizedby first taking a power spectrum of a time-dependent ACC waveformgenerated by an accelerometer, normalizing it, and then separating thefractional power into frequency bands according to Table 2, below:

TABLE 2 predictor variables and their relationship to the accelerometersignal predictor variable Description x₁ normalized power of the ACcomponent of the time-dependent accelerometer signal x₂ average armangle measured while time- dependent accelerometer signal is collectedx₃ standard deviation of the arm angle while time-dependentaccelerometer signal is collected x₄ fractional power of the ACcomponent of the frequency-dependent accelerometer signal between0.5-1.0 Hz x₅ fractional power of the AC component of thefrequency-dependent accelerometer signal between 1.0-2.0 Hz x₆fractional power of the AC component of the frequency-dependentaccelerometer signal between 2.0-3.0 Hz x₇ fractional power of the ACcomponent of the frequency-dependent accelerometer signal between3.0-4.0 Hz x₈ fractional power of the AC component of thefrequency-dependent accelerometer signal between 4.0-5.0 Hz x₉fractional power of the AC component of the frequency-dependentaccelerometer signal between 5.0-6.0 Hz x₁₀ fractional power of the ACcomponent of the frequency-dependent accelerometer signal between6.0-7.0 HzThe predictor variables described in Table 2 are typically determinedfrom ACC signals generated by accelerometers deployed in locations thatare most affected by patient motion. Such accelerometers are typicallymounted directly on the wrist-worn transceiver, and on the bulkheadconnector attached to the patient's arm. The normalized signal power(x₁) for the AC components (y_(W,i), i=x,y,z) calculated from the ACC isshown in equation (39), where F_(s) denotes the signal samplingfrequency, N is the size of the data buffer, and x_(norm) is apredetermined power value:

$\begin{matrix}{x_{1} = {\frac{1}{x_{norm}}( \frac{F_{s}}{N} ){\sum\limits_{n = 1}^{N}\;\lbrack {( {y_{W,x}\lbrack n\rbrack} )^{2} + ( {y_{W,y}\lbrack n\rbrack} )^{2} + ( {y_{W,z}\lbrack n\rbrack} )^{2}} \rbrack}}} & (39)\end{matrix}$The average arm angle predictor value (x₂) was determined using equation(40):

$\begin{matrix}{x_{2} = {( \frac{1}{N} ){\sum\limits_{n = 1}^{N}{\cos( {\theta_{GW}\lbrack n\rbrack} )}}}} & (40)\end{matrix}$Note that, for this predictor value, it is unnecessary to explicitlydetermine the angle _(GW) using an arccosine function, and the readilyavailable cosine value calculated in equation (12) acts as a surrogateparameter indicating the mean arm angle. The predictor value indicatingthe standard deviation of the arm angle (x₃) was determined usingequation (41) using the same assumptions for the angle _(GW) asdescribed above:

$\begin{matrix}{x_{3} = \sqrt{( \frac{1}{N} ){\sum\limits_{n = 1}^{N}( {{\cos( {\theta_{GW}\lbrack n\rbrack} )} - x_{2}} )^{2}}}} & (41)\end{matrix}$

The remaining predictor variables (x₄-x₁₀) are determined from thefrequency content of the patient's motion, determined from the powerspectrum of the time-dependent accelerometer signals, as indicated inFIGS. 11B and 11D. To simplify implementation of this methodology, it istypically only necessary to process a single channel of the ACCwaveform. Typically, the single channel that is most affected by patientmotion is y_(W), which represents motion along the long axis of thepatient's lower arm, determined from the accelerometer mounted directlyin the wrist-worn transceiver. Determining the power requires taking anN-point Fast Fourier Transform (FFT) of the accelerometer data(X_(W)[m]); a sample FFT data point is indicated by equation (42):X _(W) [m]=a _(m) +ib _(m)  (42)Once the FFT is determined from the entire time-domain ACC waveform, thefractional power in the designated frequency band is given by equation(43), which is based on Parseval's theorem. The term mStart refers tothe FFT coefficient index at the start of the frequency band ofinterest, and the term mEnd refers to the FFT coefficient index at theend of the frequency band of interest:

$\begin{matrix}{x_{k} = {( \frac{1}{P_{T}} ){\sum\limits_{m = {mStart}}^{mEnd}\;{( {a_{m} + {ib}_{m}} )( {a_{m} - {ib}_{m}} )}}}} & (43)\end{matrix}$Finally, the formula for the total signal power, P_(T), is given inequation (44):

$\begin{matrix}{P_{T} = {\sum\limits_{m = 0}^{N/2}\;{( {a_{m} + {ib}_{m}} )( {a_{m} - {ib}_{m}} )}}} & (44)\end{matrix}$

As described above, to accurately estimate a patient's activity level,predictor values x₁-x₁₀ defined above are measured from a variety ofsubjects selected from a range of demographic criteria (e.g., age,gender, height, weight), and then processed using predeterminedregression coefficients (b_(j)) to calculate a logit variable (definedin equation (37)) and the corresponding probability outcome (defined inequation (36)). A threshold value is then determined empirically from anROC curve. The classification is declared true if the model output isgreater than or equal to the threshold value. During an actualmeasurement, an accelerometer signal is measured and then processed asdescribed above to determine the predictor values. These parameters areused to determine the logit and corresponding probability, which is thencompared to a threshold value to estimate the patient's activity level.

FIGS. 13A,B show actual ROC curves, determined using accelerometersplaced on the upper and lower arms of a collection of patients. An idealROC curve indicates a high true positive rate (shown on the y-axis) anda low false positive rate (shown on the x-axis), and thus has a shapeclosely representing a 90 degree angle. From such a curve a relativelyhigh threshold can be easily determined and used as described above todetermine a patient's activity level. Ultimately this results in ameasurement that yields a high percentage of ‘true positives’, and a lowpercentage of ‘false positives’. FIG. 13A shows, for example, a ROCcurve generated from the patients' upper 192 and lower 190 arms duringwalking. Data points on the curves 190, 192 were generated withaccelerometers and processed with algorithms as described above. Thedistribution of these data indicates that this approach yields a highselectivity for determining whether or not a patient is walking.

FIG. 13B shows data measured during resting. The ACC waveforms measuredfor this activity state feature fewer well-defined frequency componentscompared to those measured for FIG. 13A, mostly because the act of‘resting’ is not as well defined as that of ‘walking’. That is why theROC curves measured from the upper 194 and lower 196 arms have less ofan idealized shape. Still, from these data threshold values can bedetermined that can be used for future measurements to accuratelycharacterize whether or not the patient is resting.

ROC curves similar to those shown in FIGS. 13A,B can be generatedempirically from a set of patients undergoing a variety of differentactivity states. These states include, among others, falling,convulsing, running, eating, and undergoing a bowel movement. Athreshold value for each activity state is determined once the ROC curveis generated, and going forward this information can be incorporated inan algorithm for estimating the patient's activity. Such an algorithm,e.g., can be uploaded wirelessly to the wrist-worn transceiver.

FIG. 14 is a flow chart 200 showing the various algorithms describedabove, along with other supporting algorithms described in co-pendingpatent application, the contents of which are fully incorporated hereinby reference.

The initiation phase of the algorithm begins with collection oftime-dependent PPG, ECG, ACC, and pressure waveforms using analog anddigital circuitry within the body-worn vital sign monitor describedabove (step 201). An optical sensor attached to the patient's thumbmeasures PPG waveforms, while an ECG circuit attached to threeelectrodes on the patient's chest measures ECG waveforms. Oncecollected, these waveforms are digitally filtered according to step 202using standard frequency-domain techniques to remove any out-of-bandnoise. The pressure waveform, generated during an indexing measurementduring step 204 using a pneumatic system and cuff wrapped around thepatient's bicep, is measured during inflation and processed usingoscillometry, as described in the above-referenced patient applicationentitled: ‘VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USINGOPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS’ (U.S. Ser. No. 12/138,194;filed Jun. 12, 2008), the contents of which have been previouslyincorporated herein by reference. This yields an indirect measurement ofSYS, DIA, and MAP values. Alternatively, SYS can be determined directlyby processing the PPG in the presence of applied pressure according tostep 206 (as described in patent application referenced immediatelyabove). PTT is measured as a function of applied pressure during theindexing measurement, and is processed during step 208 according todetermine a personal, patient-specific slope (as described in patentapplication referenced immediately above). This slope, along with bloodpressure values determined with oscillometry during the indexingmeasurement, is used along with PTT values measured from a temporalseparation between the ECG and PPG to determine cNIBP according to step220 (as described in patent application referenced immediately above).

Motion, as described in detail above, can complicate measurement of theabove-described parameters, and is determined by processingtime-dependent ACC signals from multiple accelerometers attached to thepatient and connected to the body-worn vital sign monitor. These signalsare processed according to steps 210, 212, and 214, as described indetail above, to determine the degree of motion-based signal corruption,and according to step 218 to determine posture, arm height, and activitylevel. If motion is determined to be present, cNIBP can be estimatedaccording to step 216 using a read-through technique.

SpO2 is measured according to step 222 with the body-worn vital signmonitor using an integrated reference hardware design, algorithm, andcode base provided by OSI of Hawthorne, Calif. Conventional algorithmsfor SpO2 are optimized for measurements made at the tip of the patient'sindex finger. However, since optical measurements with the body-wornvital sign monitor described above are typically made from the base ofthe patient's thumb, a modified set of calibration parameters need to beexperimentally determined for this location using informal clinicaltrials.

The above-described measurements for PTT-based cNIBP are performedaccording to step 220 by collecting data for 20-second periods, and thenprocessing these data with a variety of statistical averagingtechniques. Pressure-dependent indexing measurements according to steps204 and 206 are performed every 4 hours. In the algorithm describedabove, a technique for rolling averages can be deployed, allowing valuesfor cNIBP (step 220), HR and TEMP (step 226), RR (step 224), and SpO2(step 222) to be displayed every second. The interval forpressure-dependent indexing measurements may be extended past fourhours.

In addition to those methods described above, a number of additionalmethods can be used to calculate blood pressure from the optical andelectrical waveforms. These are described in the following co-pendingpatent applications, the contents of which are incorporated herein byreference: 1) CUFFLESS BLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS,INTERNET-BASED SYSTEM (U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2)CUFFLESS SYSTEM FOR MEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014;filed Apr. 7, 2004); 3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYINGWEB SERVICES INTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004);4) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILEDEVICE (U.S. Ser. No. 10/967,511; filed Oct. 18, 2004); 5) BLOODPRESSURE MONITORING DEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S.Ser. No. 10/967,610; filed Oct. 18, 2004); 6) PERSONAL COMPUTER-BASEDVITAL SIGN MONITOR (U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 7)PATCH SENSOR FOR MEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No.10/906,315; filed Feb. 14, 2005); 8) PATCH SENSOR FOR MEASURING VITALSIGNS (U.S. Ser. No. 11/160,957; filed Jul. 18, 2005); 9) WIRELESS,INTERNET-BASED SYSTEM FOR MEASURING VITAL SIGNS FROM A PLURALITY OFPATIENTS IN A HOSPITAL OR MEDICAL CLINIC (U.S. Ser. No. 11/162,719;filed Sep. 9, 2005); 10) HAND-HELD MONITOR FOR MEASURING VITAL SIGNS(U.S. Ser. No. 11/162,742; filed Sep. 21, 2005); 11) CHEST STRAP FORMEASURING VITAL SIGNS (U.S. Ser. No. 11/306,243; filed Dec. 20, 2005);12) SYSTEM FOR MEASURING VITAL SIGNS USING AN OPTICAL MODULE FEATURING AGREEN LIGHT SOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3, 2006); 13)BILATERAL DEVICE, SYSTEM AND METHOD FOR MONITORING VITAL SIGNS (U.S.Ser. No. 11/420,281; filed May 25, 2006); 14) SYSTEM FOR MEASURING VITALSIGNS USING BILATERAL PULSE TRANSIT TIME (U.S. Ser. No. 11/420,652;filed May 26, 2006); 15) BLOOD PRESSURE MONITOR (U.S. Ser. No.11/530,076; filed Sep. 8, 2006); 16) TWO-PART PATCH SENSOR FORMONITORING VITAL SIGNS (U.S. Ser. No. 11/558,538; filed Nov. 10, 2006);and, 17) MONITOR FOR MEASURING VITAL SIGNS AND RENDERING VIDEO IMAGES(U.S. Ser. No. 11/682,177; filed Mar. 5, 2007).

Other embodiments are also within the scope of the invention. Forexample, other techniques, such as conventional oscillometry measuredduring deflation, can be used to determine SYS for the above-describedalgorithms. In another embodiment, ‘vascular transit time’ (VTT)measured from two PPG waveforms can be used in place of PTT, asdescribed above.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A system for measuring a blood pressure from apatient, comprising: (a) a first sensor configured to generate a firsttime-dependent waveform indicative of one or more contractile propertiesof the patient's heart; (b) an electrical sensor comprising: at leasttwo electrodes configured to detect electrical signals from thepatient's body, and an electrical circuit operably connected to theelectrodes and configured to process the detected electrical signals,the electrical sensor generating a time dependent electrical waveformindicative of one or more contractile properties of the patient's heart;(c) at least two motion-detecting sensors positioned on separatelocations on the patient's body, each of said motion-detecting sensorsgenerating at least one time-dependent motion waveform indicative ofmotion of the location on the patient's body to which the motiondetecting sensor is affixed; and, (d) a processing component configuredto be worn on the patient's body and comprising a microprocessor, saidprocessing component configured to receive the first time-dependentwaveform, the time-dependent electrical waveform, and the at least onetime-dependent motion waveform generated by each motion-detectingsensor, and to determine: (i) a first arm position from one or more ofthe time-dependent motion waveforms acquired in a first arm position;(ii) a first time difference between a feature present in each of thefirst time-dependent waveform and the first time-dependent electricalwaveform acquired in the first arm position; (iii) a second arm positionfrom one or more of the time-dependent motion waveforms acquired in asecond arm position; (iv) a second time difference between a featurepresent in each of the first time dependent waveform and the firsttime-dependent electrical waveform acquired in the second arm position;(v) a first blood pressure value calculated from the first timedifference; (vi) a second blood pressure value calculated from thesecond time difference; (vii) a blood pressure factor calculated usingthe first blood pressure value and the first time difference or aproperty derived therefrom, together with the second blood pressurevalue and the second time difference or a property derived therefrom. 2.The system of claim 1, wherein the processing component furthercomprises a display component.
 3. The system of claim 1, wherein thedisplay component renders a graphic interface configured to instruct thepatient to move an arm to the first arm position and then to the secondarm position.
 4. The system of claim 1, wherein the processing componentfurther comprises an audio component.
 5. The system of claim 4, whereinthe audio component renders audible instructions that instruct thepatient to move an arm to the first arm position and then to the secondarm position.
 6. The system of claim 1, wherein the processing componentis further configured to detect when the patient's arm is in the firstposition by processing at least one time-dependent motion waveformgenerated by each motion-detecting sensor.
 7. The system of claim 6,wherein the processing component is further configured to detect whenthe patient's arm is in the second position by processing at least onetime dependent motion waveform generated by each motion-detectingsensor.
 8. The system of claim 7, wherein the processing component isfurther configured to determine a third time difference between afeature present in each of the time-dependent waveform and thetime-dependent electrical waveform after the processing componentdetermines the first and second time differences, and to determine athird blood pressure value using the third time difference and the bloodpressure factor.
 9. The system of claim 8, further comprising apneumatic system comprising an inflatable cuff, a pump, and a pressuresensor, the inflatable cuff configured to attach to the patient's armand be inflated by the pump, and the pressure sensor configured tomeasure a time dependent pressure waveform representing a pressurewithin the inflatable cuff.
 10. The system of claim 9, wherein thepressure sensor is configured to measure a time-dependent pressurewaveform representing a pressure within the inflatable cuff while thepump inflates the inflatable cuff.
 11. The system of claim 9, furthercomprising a processor configured to process the time-dependent pressurewaveform to calculate at least one cuff-based blood pressure value. 12.The system of claim 11, wherein the processing component is furtherconfigured to determine a third blood pressure value by processing thethird time difference, the blood pressure factor, and the cuff-basedblood pressure value.
 13. The system of claim 8, wherein the processingcomponent is further configured to determine a third blood pressurevalue by processing the third time difference and the blood pressurefactor.
 14. The system of claim 1, wherein a first motion-detectingsensor is positioned at a location on the patient's body selected from alower arm, wrist, hand, and finger.
 15. The system of claim 14, whereina second motion-detecting sensor is positioned at a location on thepatient's body selected from an upper arm, shoulder, and chest.
 16. Thesystem of claim 1, wherein the first sensor comprises an optical sensorcomprising a source of electromagnetic radiation configured to irradiatetissue of the patient with radiation emitted therefrom, and a detectorconfigured to detect one or more properties of the electromagneticradiation after it irradiates said tissue, the optical sensor generatinga time dependent optical waveform indicative of volumetric changes inthe irradiated tissue.
 17. The system of claim 16, wherein theprocessing component is further configured to detect a time differencebetween a feature of the time-dependent optical waveform and a featureof the time-dependent electrical waveform.